Predicting the duration of traffic accidents can effectively help traffic management. To make a more accurate real-time prediction of traffic accident duration, and fully utilize the huge amount of traffic texts in social networks, in this paper, we consider this prediction task as a classification problem. First, the reported text of traffic accidents in social networks is obtained. After the data augmentation, the Bag-of-words model and Fisher optimal segmentation algorithm are combined to calculate the optimal classification threshold based on duration, and the accidents are classified into four classes. And then, the C-BiLSTM neural network is constructed by fusing convolutional neural network (CNN) and bidirectional long short term memory (Bi-LSTM) to predict the classes of accident durations, and the prediction accuracy of final trained model can reach 96.09%. Through experiments, the proposed method is proved to be practical and effective in solving traffic accident duration prediction.


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    Title :

    Traffic accident duration prediction based on natural language processing and a hybrid neural network architecture


    Contributors:
    Xiao, Siyao (author)

    Conference:

    2021 International Conference on Neural Networks, Information and Communication Engineering ; 2021 ; Qingdao,China


    Published in:

    Proc. SPIE ; 11933


    Publication date :

    2021-10-15





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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